{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Pipelines, Tuning, and AutoML\n",
    "This notebook is about pipelining and tuning (grid search) for time series forecasting with `sktime`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Using exogeneous data `X` in forecasting\n",
    "The exogenous data `X` is a `pd.DataFrame` with the same index as `y`. It can be given optionally to a forecaster in the `fit` and `predict` methods. If `X` is given in `fit`, it also has to be given in `predict`, this means the future values of `X` have to be available at the time of prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datasets import load_longley\n",
    "from sktime.forecasting.base import ForecastingHorizon\n",
    "from sktime.split import temporal_train_test_split\n",
    "from sktime.utils.plotting import plot_series\n",
    "\n",
    "y, X = load_longley()\n",
    "y_train, y_test, X_train, X_test = temporal_train_test_split(y=y, X=X, test_size=6)\n",
    "fh = ForecastingHorizon(y_test.index, is_relative=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GNPDEFL</th>\n",
       "      <th>GNP</th>\n",
       "      <th>UNEMP</th>\n",
       "      <th>ARMED</th>\n",
       "      <th>POP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>83.0</td>\n",
       "      <td>234289.0</td>\n",
       "      <td>2356.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>107608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>88.5</td>\n",
       "      <td>259426.0</td>\n",
       "      <td>2325.0</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>108632.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>88.2</td>\n",
       "      <td>258054.0</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>1616.0</td>\n",
       "      <td>109773.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>89.5</td>\n",
       "      <td>284599.0</td>\n",
       "      <td>3351.0</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>110929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>96.2</td>\n",
       "      <td>328975.0</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>3099.0</td>\n",
       "      <td>112075.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        GNPDEFL       GNP   UNEMP   ARMED       POP\n",
       "Period                                             \n",
       "1947       83.0  234289.0  2356.0  1590.0  107608.0\n",
       "1948       88.5  259426.0  2325.0  1456.0  108632.0\n",
       "1949       88.2  258054.0  3682.0  1616.0  109773.0\n",
       "1950       89.5  284599.0  3351.0  1650.0  110929.0\n",
       "1951       96.2  328975.0  2099.0  3099.0  112075.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.arima import AutoARIMA\n",
    "\n",
    "forecaster = AutoARIMA(suppress_warnings=True)\n",
    "forecaster.fit(y=y_train, X=X_train)\n",
    "y_pred = forecaster.predict(fh=fh, X=X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In case we dont have future `X` values to call `predict(...)`, we can forecast `X` separately by means of the `ForecastX` composition class. This class takes two separate forecasters, one to forecast `y` and another one to forecast `X`. This means that when we call `predict(...)` first the `X` is forecasted and then given to the `y` forecaster to forecast `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.compose import ForecastX\n",
    "from sktime.forecasting.var import VAR\n",
    "\n",
    "forecaster_X = ForecastX(\n",
    "    forecaster_y=AutoARIMA(sp=1, suppress_warnings=True),\n",
    "    forecaster_X=VAR(),\n",
    ")\n",
    "forecaster_X.fit(y=y, X=X, fh=fh)\n",
    "# now in predict() we don't need to pass X\n",
    "y_pred = forecaster_X.predict(fh=fh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Simple Forecasting Pipelines\n",
    "\n",
    "We have seen: pipelines combine multiple estimators into one.\n",
    "\n",
    "Forecasters methods have two key data arguments, both time series:\n",
    "\n",
    "* endogeneous data, `y`\n",
    "* exogeneous data `X`\n",
    "\n",
    "Pipelines exist for both"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline transformers on endogenous data - `y` argument \n",
    "\n",
    "Sequence of transforms without pipeline object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.arima import AutoARIMA\n",
    "from sktime.transformations.series.detrend import Deseasonalizer, Detrender\n",
    "\n",
    "detrender = Detrender()\n",
    "deseasonalizer = Deseasonalizer()\n",
    "forecaster = AutoARIMA(sp=1, suppress_warnings=True)\n",
    "\n",
    "# fit_transform\n",
    "y_train_1 = detrender.fit_transform(y_train)\n",
    "y_train_2 = deseasonalizer.fit_transform(y_train_1)\n",
    "\n",
    "# fit\n",
    "forecaster.fit(y_train_2)\n",
    "\n",
    "# predidct\n",
    "y_pred_2 = forecaster.predict(fh)\n",
    "\n",
    "# inverse_transform\n",
    "y_pred_1 = detrender.inverse_transform(y_pred_2)\n",
    "y_pred_isolated = deseasonalizer.inverse_transform(y_pred_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one end-to-end forecaster, using `TransformedTargetForecaster` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.compose import TransformedTargetForecaster\n",
    "\n",
    "pipe_y = TransformedTargetForecaster(\n",
    "    steps=[\n",
    "        (\"detrend\", Detrender()),\n",
    "        (\"deseasonalize\", Deseasonalizer()),\n",
    "        (\"forecaster\", AutoARIMA(sp=1, suppress_warnings=True)),\n",
    "    ]\n",
    ")\n",
    "pipe_y.fit(y=y_train)\n",
    "y_pred_pipe = pipe_y.predict(fh=fh)\n",
    "\n",
    "from pandas.testing import assert_series_equal\n",
    "\n",
    "# make sure that outputs are the same\n",
    "assert_series_equal(y_pred_pipe, y_pred_isolated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline transformers on exogenous data - `X` argument\n",
    "\n",
    "Applies transformers to `X` argument before passed to forecaster.\n",
    "\n",
    "Here: `Detrender` then `Deseasonalizer`, then passed to `AutoARIMA`'s `X`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.compose import ForecastingPipeline\n",
    "\n",
    "pipe_X = ForecastingPipeline(\n",
    "    steps=[\n",
    "        (\"detrend\", Detrender()),\n",
    "        (\"deseasonalize\", Deseasonalizer()),\n",
    "        (\"forecaster\", AutoARIMA(sp=1, suppress_warnings=True)),\n",
    "    ]\n",
    ")\n",
    "pipe_X.fit(y=y_train, X=X_train)\n",
    "y_pred = pipe_X.predict(fh=fh, X=X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline on `y` and `X` data by means of nesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_y = TransformedTargetForecaster(\n",
    "    steps=[\n",
    "        (\"detrend\", Detrender()),\n",
    "        (\"deseasonalize\", Deseasonalizer()),\n",
    "        (\"forecaster\", AutoARIMA(sp=1, suppress_warnings=True)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipe_X = ForecastingPipeline(\n",
    "    steps=[\n",
    "        (\"detrend\", Detrender()),\n",
    "        (\"deseasonalize\", Deseasonalizer()),\n",
    "        (\"forecaster\", pipe_y),\n",
    "    ]\n",
    ")\n",
    "pipe_X.fit(y=y_train, X=X_train)\n",
    "y_pred = pipe_X.predict(fh=fh, X=X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sktime` provides a dunder methods for estimators to chain them into pipelines and other compositions. To create pipelines we can use `*` for creation of a `TransformedTargetForecaster` and `**` for creation of a `ForecastingPipeline`. Further dunder methods are given in the appendinx section at the end of this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformedTargetForecaster(steps=[Detrender(), Deseasonalizer(), AutoARIMA()])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[Detrender(), Deseasonalizer(), AutoARIMA()])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TransformedTargetForecaster(steps=[Detrender(), Deseasonalizer(), AutoARIMA()])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_y = Detrender() * Deseasonalizer() * AutoARIMA()\n",
    "pipe_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ForecastingPipeline(steps=[Detrender(), Deseasonalizer(), AutoARIMA()])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ForecastingPipeline</label><div class=\"sk-toggleable__content\"><pre>ForecastingPipeline(steps=[Detrender(), Deseasonalizer(), AutoARIMA()])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ForecastingPipeline(steps=[Detrender(), Deseasonalizer(), AutoARIMA()])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_X = Detrender() ** Deseasonalizer() ** AutoARIMA()\n",
    "pipe_X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nesting with dunders, both `X` and `y`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_nested = (\n",
    "    Detrender() * Deseasonalizer() * (Detrender() ** Deseasonalizer() ** AutoARIMA())\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporal cross-validation\n",
    "\n",
    "In `sktime` there are three different types of temporal cross-validation splitters available:\n",
    "- `SingleWindowSplitter`, which is equivalent to a single train-test-split\n",
    "- `SlidingWindowSplitter`, which is using a rolling window approach and \"forgets\" the oldest observations as we move more into the future\n",
    "- `ExpandingWindowSplitter`, which is using a expanding window approach and keep all observations in the training set as we move more into the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sktime.datasets import load_shampoo_sales\n",
    "\n",
    "y = load_shampoo_sales()\n",
    "y_train, y_test = temporal_train_test_split(y=y, test_size=6)\n",
    "plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.base import ForecastingHorizon\n",
    "from sktime.split import (\n",
    "    ExpandingWindowSplitter,\n",
    "    SingleWindowSplitter,\n",
    "    SlidingWindowSplitter,\n",
    ")\n",
    "from sktime.utils.plotting import plot_windows\n",
    "\n",
    "fh = ForecastingHorizon(y_test.index, is_relative=False).to_relative(\n",
    "    cutoff=y_train.index[-1]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = SingleWindowSplitter(fh=fh, window_length=len(y_train) - 6)\n",
    "plot_windows(cv=cv, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = SlidingWindowSplitter(fh=fh, window_length=12, step_length=1)\n",
    "plot_windows(cv=cv, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = ExpandingWindowSplitter(fh=fh, initial_window=12, step_length=1)\n",
    "plot_windows(cv=cv, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get number of total splits (folds)\n",
    "cv.get_n_splits(y=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid search\n",
    "Performing a grid search can be done in `sktime` equivalent to `sklearn` by using a cross-validation to search for the best parameter combination. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 13 folds for each of 24 candidates, totalling 312 fits\n"
     ]
    }
   ],
   "source": [
    "from sktime.forecasting.exp_smoothing import ExponentialSmoothing\n",
    "from sktime.forecasting.model_selection import ForecastingGridSearchCV\n",
    "from sktime.performance_metrics.forecasting import MeanSquaredError\n",
    "\n",
    "forecaster = ExponentialSmoothing()\n",
    "param_grid = {\n",
    "    \"sp\": [4, 6, 12],\n",
    "    \"seasonal\": [\"add\", \"mul\"],\n",
    "    \"trend\": [\"add\", \"mul\"],\n",
    "    \"damped_trend\": [True, False],\n",
    "}\n",
    "gscv = ForecastingGridSearchCV(\n",
    "    forecaster=forecaster,\n",
    "    param_grid=param_grid,\n",
    "    cv=cv,\n",
    "    verbose=1,\n",
    "    scoring=MeanSquaredError(square_root=True),\n",
    ")\n",
    "gscv.fit(y_train)\n",
    "y_pred = gscv.predict(fh=fh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'damped_trend': True, 'seasonal': 'add', 'sp': 12, 'trend': 'mul'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExponentialSmoothing(damped_trend=True, seasonal=&#x27;add&#x27;, sp=12, trend=&#x27;mul&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExponentialSmoothing</label><div class=\"sk-toggleable__content\"><pre>ExponentialSmoothing(damped_trend=True, seasonal=&#x27;add&#x27;, sp=12, trend=&#x27;mul&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExponentialSmoothing(damped_trend=True, seasonal='add', sp=12, trend='mul')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_forecaster_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_test_MeanSquaredError</th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_pred_time</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_MeanSquaredError</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98.635359</td>\n",
       "      <td>0.126247</td>\n",
       "      <td>0.004085</td>\n",
       "      <td>{'damped_trend': True, 'seasonal': 'add', 'sp'...</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.327241</td>\n",
       "      <td>0.137226</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>{'damped_trend': True, 'seasonal': 'add', 'sp'...</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104.567201</td>\n",
       "      <td>0.127894</td>\n",
       "      <td>0.004397</td>\n",
       "      <td>{'damped_trend': True, 'seasonal': 'add', 'sp'...</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>112.555601</td>\n",
       "      <td>0.137103</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>{'damped_trend': True, 'seasonal': 'add', 'sp'...</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>147.065358</td>\n",
       "      <td>0.107994</td>\n",
       "      <td>0.003920</td>\n",
       "      <td>{'damped_trend': True, 'seasonal': 'add', 'sp'...</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_test_MeanSquaredError  mean_fit_time  mean_pred_time  \\\n",
       "0                   98.635359       0.126247        0.004085   \n",
       "1                   98.327241       0.137226        0.004455   \n",
       "2                  104.567201       0.127894        0.004397   \n",
       "3                  112.555601       0.137103        0.004005   \n",
       "4                  147.065358       0.107994        0.003920   \n",
       "\n",
       "                                              params  \\\n",
       "0  {'damped_trend': True, 'seasonal': 'add', 'sp'...   \n",
       "1  {'damped_trend': True, 'seasonal': 'add', 'sp'...   \n",
       "2  {'damped_trend': True, 'seasonal': 'add', 'sp'...   \n",
       "3  {'damped_trend': True, 'seasonal': 'add', 'sp'...   \n",
       "4  {'damped_trend': True, 'seasonal': 'add', 'sp'...   \n",
       "\n",
       "   rank_test_MeanSquaredError  \n",
       "0                         7.0  \n",
       "1                         6.0  \n",
       "2                        13.0  \n",
       "3                        16.0  \n",
       "4                        22.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.cv_results_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid search with pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For tuning parameters with compositions such as pipelines, we can use the \\<estimator\\>__\\<parameter\\> syntax known from [scikit-learn](https://scikit-learn.org/stable/modules/grid_search.html#composite-estimators-and-parameter-spaces). For multiple levels of nesting, we can use the same syntax with two underscores, e.g. `forecaster__transformer__parameter`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 13 folds for each of 192 candidates, totalling 2496 fits\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, PowerTransformer, RobustScaler\n",
    "\n",
    "from sktime.forecasting.compose import TransformedTargetForecaster\n",
    "from sktime.transformations.series.adapt import TabularToSeriesAdaptor\n",
    "from sktime.transformations.series.detrend import Deseasonalizer, Detrender\n",
    "\n",
    "forecaster = TransformedTargetForecaster(\n",
    "    steps=[\n",
    "        (\"detrender\", Detrender()),\n",
    "        (\"deseasonalizer\", Deseasonalizer()),\n",
    "        (\"minmax\", TabularToSeriesAdaptor(MinMaxScaler((1, 10)))),\n",
    "        (\"power\", TabularToSeriesAdaptor(PowerTransformer())),\n",
    "        (\"scaler\", TabularToSeriesAdaptor(RobustScaler())),\n",
    "        (\"forecaster\", ExponentialSmoothing()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# using dunder notation to access inner objects/params as in sklearn\n",
    "param_grid = {\n",
    "    # deseasonalizer\n",
    "    \"deseasonalizer__model\": [\"multiplicative\", \"additive\"],\n",
    "    # power\n",
    "    \"power__transformer__method\": [\"yeo-johnson\", \"box-cox\"],\n",
    "    \"power__transformer__standardize\": [True, False],\n",
    "    # forecaster\n",
    "    \"forecaster__sp\": [4, 6, 12],\n",
    "    \"forecaster__seasonal\": [\"add\", \"mul\"],\n",
    "    \"forecaster__trend\": [\"add\", \"mul\"],\n",
    "    \"forecaster__damped_trend\": [True, False],\n",
    "}\n",
    "\n",
    "gscv = ForecastingGridSearchCV(\n",
    "    forecaster=forecaster,\n",
    "    param_grid=param_grid,\n",
    "    cv=cv,\n",
    "    verbose=1,\n",
    "    scoring=MeanSquaredError(square_root=True),  # set custom scoring function\n",
    ")\n",
    "gscv.fit(y_train)\n",
    "y_pred = gscv.predict(fh=fh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'deseasonalizer__model': 'additive',\n",
       " 'forecaster__damped_trend': False,\n",
       " 'forecaster__seasonal': 'add',\n",
       " 'forecaster__sp': 4,\n",
       " 'forecaster__trend': 'add',\n",
       " 'power__transformer__method': 'yeo-johnson',\n",
       " 'power__transformer__standardize': False}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model selection\n",
    "\n",
    "There are three ways ways to select a model out of multiple models:\n",
    "1) `ForecastingGridSearchCV`\n",
    "2) `MultiplexForecaster`\n",
    "3) `relative_loss` or `RelativeLoss`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) `ForecastingGridSearchCV`\n",
    "We can use `ForecastingGridSearchCV` to fit multiple forecasters and find the best one using equivalent notations as in `sklearn`. We can either do: `param_grid: List[Dict]` or we just have a list of forecasters in the grid: `forecaster\": [NaiveForecaster(), STLForecaster()]`. The advanteage doing this together tuning the other parameters is that we can use the same cross-validation as for the other parameters to find the overall best forecasters and parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'forecaster': NaiveForecaster(sp=4),\n",
       " 'forecaster__sp': 4,\n",
       " 'forecaster__strategy': 'last',\n",
       " 'scaler__transformer__with_scaling': True}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.forecasting.naive import NaiveForecaster\n",
    "from sktime.forecasting.theta import ThetaForecaster\n",
    "from sktime.forecasting.trend import STLForecaster\n",
    "\n",
    "forecaster = TransformedTargetForecaster(\n",
    "    steps=[\n",
    "        (\"detrender\", Detrender()),\n",
    "        (\"deseasonalizer\", Deseasonalizer()),\n",
    "        (\"scaler\", TabularToSeriesAdaptor(RobustScaler())),\n",
    "        (\"minmax2\", TabularToSeriesAdaptor(MinMaxScaler((1, 10)))),\n",
    "        (\"forecaster\", NaiveForecaster()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "gscv = ForecastingGridSearchCV(\n",
    "    forecaster=forecaster,\n",
    "    param_grid=[\n",
    "        {\n",
    "            \"scaler__transformer__with_scaling\": [True, False],\n",
    "            \"forecaster\": [NaiveForecaster()],\n",
    "            \"forecaster__strategy\": [\"drift\", \"last\", \"mean\"],\n",
    "            \"forecaster__sp\": [4, 6, 12],\n",
    "        },\n",
    "        {\n",
    "            \"scaler__transformer__with_scaling\": [True, False],\n",
    "            \"forecaster\": [STLForecaster(), ThetaForecaster()],\n",
    "            \"forecaster__sp\": [4, 6, 12],\n",
    "        },\n",
    "    ],\n",
    "    cv=cv,\n",
    ")\n",
    "gscv.fit(y)\n",
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) `MultiplexForecaster`\n",
    "We can use the `MultiplexForecaster` to compare the performance of different forecasters. This approach might be useful if we want to compare the performance of different forecasters that have been tuned and fitted already separately. The `MultiplexForecaster` is just a forecaster compostition that provides a parameters `selected_forecaster: List[str]` that can be tuned with a grid search. The other parameters of the forecasters are not tuned.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'selected_forecaster': 'theta'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.forecasting.compose import MultiplexForecaster\n",
    "\n",
    "forecaster = MultiplexForecaster(\n",
    "    forecasters=[\n",
    "        (\"naive\", NaiveForecaster()),\n",
    "        (\"stl\", STLForecaster()),\n",
    "        (\"theta\", ThetaForecaster()),\n",
    "    ]\n",
    ")\n",
    "gscv = ForecastingGridSearchCV(\n",
    "    forecaster=forecaster,\n",
    "    param_grid={\"selected_forecaster\": [\"naive\", \"stl\", \"theta\"]},\n",
    "    cv=cv,\n",
    ")\n",
    "gscv.fit(y)\n",
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) `relative_loss` or `RelativeLoss`\n",
    "We can compare two models on a given test data by means of a relative loss calculation. This is however not doing any cross-validation compared to the above model selection approaches 1) and 2).\n",
    "\n",
    "The relative loss function applies a forecasting performance metric to a set of forecasts and benchmark forecasts and reports the ratio of the metric from the forecasts to the the metric from the benchmark forecasts. Relative loss output is non-negative floating point. The best value is 0.0. The relative loss function uses the `mean_absolute_error` by default as error metric. If the score is `>1`, the forecast given as `y_pred` is worse than the given benchmark forecast in `y_pred_benchmark`. If we want to customize the relative loss, we can use the `RelativeLoss` scorer class and e.g. provide a custom loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123.00771294252276"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.performance_metrics.forecasting import mean_squared_error, relative_loss\n",
    "\n",
    "relative_loss(y_true=y_test, y_pred=y_pred_1, y_pred_benchmark=y_pred_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14720.015012346896"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.performance_metrics.forecasting import RelativeLoss\n",
    "\n",
    "relative_mse = RelativeLoss(relative_loss_function=mean_squared_error)\n",
    "relative_mse(y_true=y_test, y_pred=y_pred_1, y_pred_benchmark=y_pred_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning pipeline structure as a hyper-parameter\n",
    "\n",
    "Pipeline structure choices influence performance\n",
    "\n",
    "`sktime` allows to expose these choices via structural compositors:\n",
    "\n",
    "* switch between transform/forecast: `MultiplexTransformer`, `MultiplexForecaster`\n",
    "* transformer on/off: `OptionalPassthrough`\n",
    "* sequence of transformers: `Permute`\n",
    "\n",
    "Combine with pipelines and `FeatureUnion` for rich structure space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combination of transformers\n",
    "Given a set of four different transformers, we would like to know which combination of the four transformers is having the best error. So in total there are 4² = 16 different combinations. We can use the `GridSearchCV` to find the best combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = [\n",
    "    (\"detrender\", Detrender()),\n",
    "    (\"deseasonalizer\", Deseasonalizer()),\n",
    "    (\"power\", TabularToSeriesAdaptor(PowerTransformer())),\n",
    "    (\"scaler\", TabularToSeriesAdaptor(RobustScaler())),\n",
    "    (\"forecaster\", ExponentialSmoothing()),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `sktime` there is a transformer composition called `OptionalPassthrough()` which gets a transformer as an argument and a param `passthrough: bool`. Setting `passthrough=True` will return an identity transformation for the given data. Setting `passthrough=False` will apply the given inner transformer on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-03    183.1\n",
       "1991-04    119.3\n",
       "1991-05    180.3\n",
       "Freq: M, Name: Number of shampoo sales, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.transformations.compose import OptionalPassthrough\n",
    "\n",
    "transformer = OptionalPassthrough(transformer=Detrender(), passthrough=True)\n",
    "transformer.fit_transform(y_train).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-03    183.1\n",
       "1991-04    119.3\n",
       "1991-05    180.3\n",
       "Freq: M, Name: Number of shampoo sales, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1991-01    130.376344\n",
       "1991-02      1.503263\n",
       "1991-03     29.930182\n",
       "1991-04    -42.642900\n",
       "1991-05      9.584019\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = OptionalPassthrough(transformer=Detrender(), passthrough=False)\n",
    "transformer.fit_transform(y_train).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 13 folds for each of 16 candidates, totalling 208 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ForecastingGridSearchCV(cv=ExpandingWindowSplitter(fh=ForecastingHorizon([1, 2, 3, 4, 5, 6], dtype=&#x27;int64&#x27;, is_relative=True),\n",
       "                                                   initial_window=12),\n",
       "                        forecaster=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                                       OptionalPassthrough(transformer=Detrender())),\n",
       "                                                                      (&#x27;deseasonalizer&#x27;,\n",
       "                                                                       OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                                                      (&#x27;power&#x27;,\n",
       "                                                                       OptionalPassthrough(transform...\n",
       "                                                                       OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                                                      (&#x27;forecaster&#x27;,\n",
       "                                                                       ExponentialSmoothing())]),\n",
       "                        n_jobs=-1,\n",
       "                        param_grid={&#x27;deseasonalizer__passthrough&#x27;: [True,\n",
       "                                                                    False],\n",
       "                                    &#x27;detrender__passthrough&#x27;: [True, False],\n",
       "                                    &#x27;power__passthrough&#x27;: [True, False],\n",
       "                                    &#x27;scaler__passthrough&#x27;: [True, False]},\n",
       "                        scoring=MeanSquaredError(square_root=True), verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ForecastingGridSearchCV</label><div class=\"sk-toggleable__content\"><pre>ForecastingGridSearchCV(cv=ExpandingWindowSplitter(fh=ForecastingHorizon([1, 2, 3, 4, 5, 6], dtype=&#x27;int64&#x27;, is_relative=True),\n",
       "                                                   initial_window=12),\n",
       "                        forecaster=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                                       OptionalPassthrough(transformer=Detrender())),\n",
       "                                                                      (&#x27;deseasonalizer&#x27;,\n",
       "                                                                       OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                                                      (&#x27;power&#x27;,\n",
       "                                                                       OptionalPassthrough(transform...\n",
       "                                                                       OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                                                      (&#x27;forecaster&#x27;,\n",
       "                                                                       ExponentialSmoothing())]),\n",
       "                        n_jobs=-1,\n",
       "                        param_grid={&#x27;deseasonalizer__passthrough&#x27;: [True,\n",
       "                                                                    False],\n",
       "                                    &#x27;detrender__passthrough&#x27;: [True, False],\n",
       "                                    &#x27;power__passthrough&#x27;: [True, False],\n",
       "                                    &#x27;scaler__passthrough&#x27;: [True, False]},\n",
       "                        scoring=MeanSquaredError(square_root=True), verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">forecaster: TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Detrender())),\n",
       "                                   (&#x27;deseasonalizer&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                   (&#x27;power&#x27;,\n",
       "                                    OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=PowerTransformer()))),\n",
       "                                   (&#x27;scaler&#x27;,\n",
       "                                    OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                   (&#x27;forecaster&#x27;, ExponentialSmoothing())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Detrender())),\n",
       "                                   (&#x27;deseasonalizer&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                   (&#x27;power&#x27;,\n",
       "                                    OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=PowerTransformer()))),\n",
       "                                   (&#x27;scaler&#x27;,\n",
       "                                    OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                   (&#x27;forecaster&#x27;, ExponentialSmoothing())])</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ForecastingGridSearchCV(cv=ExpandingWindowSplitter(fh=ForecastingHorizon([1, 2, 3, 4, 5, 6], dtype='int64', is_relative=True),\n",
       "                                                   initial_window=12),\n",
       "                        forecaster=TransformedTargetForecaster(steps=[('detrender',\n",
       "                                                                       OptionalPassthrough(transformer=Detrender())),\n",
       "                                                                      ('deseasonalizer',\n",
       "                                                                       OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                                                      ('power',\n",
       "                                                                       OptionalPassthrough(transform...\n",
       "                                                                       OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                                                      ('forecaster',\n",
       "                                                                       ExponentialSmoothing())]),\n",
       "                        n_jobs=-1,\n",
       "                        param_grid={'deseasonalizer__passthrough': [True,\n",
       "                                                                    False],\n",
       "                                    'detrender__passthrough': [True, False],\n",
       "                                    'power__passthrough': [True, False],\n",
       "                                    'scaler__passthrough': [True, False]},\n",
       "                        scoring=MeanSquaredError(square_root=True), verbose=1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecaster = TransformedTargetForecaster(\n",
    "    steps=[\n",
    "        (\"detrender\", OptionalPassthrough(Detrender())),\n",
    "        (\"deseasonalizer\", OptionalPassthrough(Deseasonalizer())),\n",
    "        (\"power\", OptionalPassthrough(TabularToSeriesAdaptor(PowerTransformer()))),\n",
    "        (\"scaler\", OptionalPassthrough(TabularToSeriesAdaptor(RobustScaler()))),\n",
    "        (\"forecaster\", ExponentialSmoothing()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "param_grid = {\n",
    "    \"detrender__passthrough\": [True, False],\n",
    "    \"deseasonalizer__passthrough\": [True, False],\n",
    "    \"power__passthrough\": [True, False],\n",
    "    \"scaler__passthrough\": [True, False],\n",
    "}\n",
    "\n",
    "gscv = ForecastingGridSearchCV(\n",
    "    forecaster=forecaster,\n",
    "    param_grid=param_grid,\n",
    "    cv=cv,\n",
    "    verbose=1,\n",
    "    scoring=MeanSquaredError(square_root=True),\n",
    ")\n",
    "gscv.fit(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best performing combination of transformers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'deseasonalizer__passthrough': True,\n",
       " 'detrender__passthrough': False,\n",
       " 'power__passthrough': True,\n",
       " 'scaler__passthrough': True}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Worst performing combination of transformers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'deseasonalizer__passthrough': False,\n",
       " 'detrender__passthrough': True,\n",
       " 'power__passthrough': False,\n",
       " 'scaler__passthrough': True}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# worst params\n",
    "gscv.cv_results_.sort_values(by=\"mean_test_MeanSquaredError\", ascending=True).iloc[-1][\n",
    "    \"params\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Permutation of transformers\n",
    "\n",
    "Given a set of four different transformers, we would like to know which permutation (ordering) of the four transformers is having the best error. In total there are `4! = 24` different permutations. We can use the `GridSearchCV` to find the best permutation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.compose import Permute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 19 folds for each of 4 candidates, totalling 76 fits\n"
     ]
    }
   ],
   "source": [
    "forecaster = TransformedTargetForecaster(\n",
    "    steps=[\n",
    "        (\"detrender\", Detrender()),\n",
    "        (\"deseasonalizer\", Deseasonalizer()),\n",
    "        (\"power\", TabularToSeriesAdaptor(PowerTransformer())),\n",
    "        (\"scaler\", TabularToSeriesAdaptor(RobustScaler())),\n",
    "        (\"forecaster\", ExponentialSmoothing()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "param_grid = {\n",
    "    \"permutation\": [\n",
    "        [\"detrender\", \"deseasonalizer\", \"power\", \"scaler\", \"forecaster\"],\n",
    "        [\"power\", \"scaler\", \"detrender\", \"deseasonalizer\", \"forecaster\"],\n",
    "        [\"scaler\", \"deseasonalizer\", \"power\", \"detrender\", \"forecaster\"],\n",
    "        [\"deseasonalizer\", \"power\", \"scaler\", \"detrender\", \"forecaster\"],\n",
    "    ]\n",
    "}\n",
    "permuted = Permute(estimator=forecaster, permutation=None)\n",
    "\n",
    "gscv = ForecastingGridSearchCV(\n",
    "    forecaster=permuted,\n",
    "    param_grid=param_grid,\n",
    "    cv=cv,\n",
    "    verbose=1,\n",
    "    scoring=MeanSquaredError(square_root=True),\n",
    ")\n",
    "\n",
    "permuted = gscv.fit(y, fh=fh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_test_MeanSquaredError</th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_pred_time</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_MeanSquaredError</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104.529303</td>\n",
       "      <td>0.119823</td>\n",
       "      <td>0.032105</td>\n",
       "      <td>{'permutation': ['detrender', 'deseasonalizer'...</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95.172818</td>\n",
       "      <td>0.118840</td>\n",
       "      <td>0.033758</td>\n",
       "      <td>{'permutation': ['power', 'scaler', 'detrender...</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95.243942</td>\n",
       "      <td>0.114363</td>\n",
       "      <td>0.033743</td>\n",
       "      <td>{'permutation': ['scaler', 'deseasonalizer', '...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.172818</td>\n",
       "      <td>0.119377</td>\n",
       "      <td>0.030739</td>\n",
       "      <td>{'permutation': ['deseasonalizer', 'power', 's...</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_test_MeanSquaredError  mean_fit_time  mean_pred_time  \\\n",
       "0                  104.529303       0.119823        0.032105   \n",
       "1                   95.172818       0.118840        0.033758   \n",
       "2                   95.243942       0.114363        0.033743   \n",
       "3                   95.172818       0.119377        0.030739   \n",
       "\n",
       "                                              params  \\\n",
       "0  {'permutation': ['detrender', 'deseasonalizer'...   \n",
       "1  {'permutation': ['power', 'scaler', 'detrender...   \n",
       "2  {'permutation': ['scaler', 'deseasonalizer', '...   \n",
       "3  {'permutation': ['deseasonalizer', 'power', 's...   \n",
       "\n",
       "   rank_test_MeanSquaredError  \n",
       "0                         4.0  \n",
       "1                         1.5  \n",
       "2                         3.0  \n",
       "3                         1.5  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permuted.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'permutation': ['power',\n",
       "  'scaler',\n",
       "  'detrender',\n",
       "  'deseasonalizer',\n",
       "  'forecaster']}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'permutation': ['detrender',\n",
       "  'deseasonalizer',\n",
       "  'power',\n",
       "  'scaler',\n",
       "  'forecaster']}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# worst params\n",
    "gscv.cv_results_.sort_values(by=\"mean_test_MeanSquaredError\", ascending=True).iloc[-1][\n",
    "    \"params\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Putting it all together: AutoML-like pipeline and tuning\n",
    "\n",
    "Taking all incredients from above examples, we can build a forecaster that comes close to what is usually called [AutoML](https://en.wikipedia.org/wiki/Automated_machine_learning).\n",
    "With AutoML we aim to automate as many steps of an ML model creation as possible. The main compositions from `sktime` that we can use for this are:\n",
    "- `TransformedTargetForecaster`\n",
    "- `ForecastingPipeline`\n",
    "- `ForecastingGridSearchCV`\n",
    "- `OptionalPassthrough`\n",
    "- `Permute`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate example\n",
    "Please see appendix section for an example with exogenous data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 13 folds for each of 192 candidates, totalling 2496 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ForecastingGridSearchCV(cv=ExpandingWindowSplitter(fh=ForecastingHorizon([1, 2, 3, 4, 5, 6], dtype=&#x27;int64&#x27;, is_relative=True),\n",
       "                                                   initial_window=12),\n",
       "                        error_score=&#x27;raise&#x27;,\n",
       "                        forecaster=Permute(estimator=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                                                         OptionalPassthrough(transformer=Detrender())),\n",
       "                                                                                        (&#x27;deseasonalizer&#x27;,\n",
       "                                                                                         OptionalPassthrough(transformer=Deseasonalizer())),...\n",
       "                                    &#x27;estimator__scaler__passthrough&#x27;: [True,\n",
       "                                                                       False],\n",
       "                                    &#x27;estimator__scaler__transformer__transformer__with_centering&#x27;: [True,\n",
       "                                                                                                    False],\n",
       "                                    &#x27;estimator__scaler__transformer__transformer__with_scaling&#x27;: [True,\n",
       "                                                                                                  False],\n",
       "                                    &#x27;permutation&#x27;: [[&#x27;detrender&#x27;,\n",
       "                                                     &#x27;deseasonalizer&#x27;, &#x27;scaler&#x27;,\n",
       "                                                     &#x27;forecaster&#x27;],\n",
       "                                                    [&#x27;scaler&#x27;, &#x27;deseasonalizer&#x27;,\n",
       "                                                     &#x27;detrender&#x27;,\n",
       "                                                     &#x27;forecaster&#x27;]]},\n",
       "                        scoring=MeanSquaredError(square_root=True), verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ForecastingGridSearchCV</label><div class=\"sk-toggleable__content\"><pre>ForecastingGridSearchCV(cv=ExpandingWindowSplitter(fh=ForecastingHorizon([1, 2, 3, 4, 5, 6], dtype=&#x27;int64&#x27;, is_relative=True),\n",
       "                                                   initial_window=12),\n",
       "                        error_score=&#x27;raise&#x27;,\n",
       "                        forecaster=Permute(estimator=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                                                         OptionalPassthrough(transformer=Detrender())),\n",
       "                                                                                        (&#x27;deseasonalizer&#x27;,\n",
       "                                                                                         OptionalPassthrough(transformer=Deseasonalizer())),...\n",
       "                                    &#x27;estimator__scaler__passthrough&#x27;: [True,\n",
       "                                                                       False],\n",
       "                                    &#x27;estimator__scaler__transformer__transformer__with_centering&#x27;: [True,\n",
       "                                                                                                    False],\n",
       "                                    &#x27;estimator__scaler__transformer__transformer__with_scaling&#x27;: [True,\n",
       "                                                                                                  False],\n",
       "                                    &#x27;permutation&#x27;: [[&#x27;detrender&#x27;,\n",
       "                                                     &#x27;deseasonalizer&#x27;, &#x27;scaler&#x27;,\n",
       "                                                     &#x27;forecaster&#x27;],\n",
       "                                                    [&#x27;scaler&#x27;, &#x27;deseasonalizer&#x27;,\n",
       "                                                     &#x27;detrender&#x27;,\n",
       "                                                     &#x27;forecaster&#x27;]]},\n",
       "                        scoring=MeanSquaredError(square_root=True), verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">forecaster: Permute</label><div class=\"sk-toggleable__content\"><pre>Permute(estimator=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                      OptionalPassthrough(transformer=Detrender())),\n",
       "                                                     (&#x27;deseasonalizer&#x27;,\n",
       "                                                      OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                                     (&#x27;scaler&#x27;,\n",
       "                                                      OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                                     (&#x27;forecaster&#x27;,\n",
       "                                                      STLForecaster())]))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Detrender())),\n",
       "                                   (&#x27;deseasonalizer&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                   (&#x27;scaler&#x27;,\n",
       "                                    OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                   (&#x27;forecaster&#x27;, STLForecaster())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Detrender())),\n",
       "                                   (&#x27;deseasonalizer&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                   (&#x27;scaler&#x27;,\n",
       "                                    OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                   (&#x27;forecaster&#x27;, STLForecaster())])</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ForecastingGridSearchCV(cv=ExpandingWindowSplitter(fh=ForecastingHorizon([1, 2, 3, 4, 5, 6], dtype='int64', is_relative=True),\n",
       "                                                   initial_window=12),\n",
       "                        error_score='raise',\n",
       "                        forecaster=Permute(estimator=TransformedTargetForecaster(steps=[('detrender',\n",
       "                                                                                         OptionalPassthrough(transformer=Detrender())),\n",
       "                                                                                        ('deseasonalizer',\n",
       "                                                                                         OptionalPassthrough(transformer=Deseasonalizer())),...\n",
       "                                    'estimator__scaler__passthrough': [True,\n",
       "                                                                       False],\n",
       "                                    'estimator__scaler__transformer__transformer__with_centering': [True,\n",
       "                                                                                                    False],\n",
       "                                    'estimator__scaler__transformer__transformer__with_scaling': [True,\n",
       "                                                                                                  False],\n",
       "                                    'permutation': [['detrender',\n",
       "                                                     'deseasonalizer', 'scaler',\n",
       "                                                     'forecaster'],\n",
       "                                                    ['scaler', 'deseasonalizer',\n",
       "                                                     'detrender',\n",
       "                                                     'forecaster']]},\n",
       "                        scoring=MeanSquaredError(square_root=True), verbose=1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_y = TransformedTargetForecaster(\n",
    "    steps=[\n",
    "        (\"detrender\", OptionalPassthrough(Detrender())),\n",
    "        (\"deseasonalizer\", OptionalPassthrough(Deseasonalizer())),\n",
    "        (\"scaler\", OptionalPassthrough(TabularToSeriesAdaptor(RobustScaler()))),\n",
    "        (\"forecaster\", STLForecaster()),\n",
    "    ]\n",
    ")\n",
    "permuted_y = Permute(estimator=pipe_y, permutation=None)\n",
    "\n",
    "param_grid = {\n",
    "    \"permutation\": [\n",
    "        [\"detrender\", \"deseasonalizer\", \"scaler\", \"forecaster\"],\n",
    "        [\"scaler\", \"deseasonalizer\", \"detrender\", \"forecaster\"],\n",
    "    ],\n",
    "    \"estimator__detrender__passthrough\": [True, False],\n",
    "    \"estimator__deseasonalizer__passthrough\": [True, False],\n",
    "    \"estimator__scaler__passthrough\": [True, False],\n",
    "    \"estimator__scaler__transformer__transformer__with_scaling\": [True, False],\n",
    "    \"estimator__scaler__transformer__transformer__with_centering\": [True, False],\n",
    "    \"estimator__forecaster__sp\": [4, 8, 12],\n",
    "}\n",
    "\n",
    "gscv = ForecastingGridSearchCV(\n",
    "    forecaster=permuted_y,\n",
    "    param_grid=param_grid,\n",
    "    cv=cv,\n",
    "    verbose=1,\n",
    "    scoring=MeanSquaredError(square_root=True),\n",
    "    error_score=\"raise\",\n",
    ")\n",
    "\n",
    "gscv.fit(y=y_train, fh=fh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.44136911735615"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.cv_results_[\"mean_test_MeanSquaredError\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125.54124792614672"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.cv_results_[\"mean_test_MeanSquaredError\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Model backtesting\n",
    "\n",
    "After fitting a model, we can evaluate the model error in the past similar to a cross-validation. For this we can use the `evaluate` function. This approach is often also called backtesting as we want to test the forecasters performance in the past."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_MeanSquaredError</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>pred_time</th>\n",
       "      <th>len_train_window</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>y_train</th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85.405220</td>\n",
       "      <td>0.008634</td>\n",
       "      <td>0.009597</td>\n",
       "      <td>12</td>\n",
       "      <td>1991-12</td>\n",
       "      <td>Period\n",
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-...</td>\n",
       "      <td>Period\n",
       "1992-01    194.3\n",
       "1992-02    149.5\n",
       "1992-...</td>\n",
       "      <td>1992-01    266.0\n",
       "1992-02    145.9\n",
       "1992-03    1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129.383456</td>\n",
       "      <td>0.010045</td>\n",
       "      <td>0.007728</td>\n",
       "      <td>18</td>\n",
       "      <td>1992-06</td>\n",
       "      <td>Period\n",
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-...</td>\n",
       "      <td>Period\n",
       "1992-07    226.0\n",
       "1992-08    303.6\n",
       "1992-...</td>\n",
       "      <td>1992-07    270.953646\n",
       "1992-08    263.653646\n",
       "19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>126.393156</td>\n",
       "      <td>0.009373</td>\n",
       "      <td>0.008245</td>\n",
       "      <td>24</td>\n",
       "      <td>1992-12</td>\n",
       "      <td>Period\n",
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-...</td>\n",
       "      <td>Period\n",
       "1993-01    339.7\n",
       "1993-02    440.4\n",
       "1993-...</td>\n",
       "      <td>1993-01    260.633333\n",
       "1993-02    215.833333\n",
       "19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>171.142305</td>\n",
       "      <td>0.012287</td>\n",
       "      <td>0.009526</td>\n",
       "      <td>30</td>\n",
       "      <td>1993-06</td>\n",
       "      <td>Period\n",
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-...</td>\n",
       "      <td>Period\n",
       "1993-07    575.5\n",
       "1993-08    407.6\n",
       "1993-...</td>\n",
       "      <td>1993-07    378.659609\n",
       "1993-08    450.927848\n",
       "19...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_MeanSquaredError  fit_time  pred_time  len_train_window   cutoff  \\\n",
       "0              85.405220  0.008634   0.009597                12  1991-12   \n",
       "1             129.383456  0.010045   0.007728                18  1992-06   \n",
       "2             126.393156  0.009373   0.008245                24  1992-12   \n",
       "3             171.142305  0.012287   0.009526                30  1993-06   \n",
       "\n",
       "                                             y_train  \\\n",
       "0  Period\n",
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-...   \n",
       "1  Period\n",
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-...   \n",
       "2  Period\n",
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-...   \n",
       "3  Period\n",
       "1991-01    266.0\n",
       "1991-02    145.9\n",
       "1991-...   \n",
       "\n",
       "                                              y_test  \\\n",
       "0  Period\n",
       "1992-01    194.3\n",
       "1992-02    149.5\n",
       "1992-...   \n",
       "1  Period\n",
       "1992-07    226.0\n",
       "1992-08    303.6\n",
       "1992-...   \n",
       "2  Period\n",
       "1993-01    339.7\n",
       "1993-02    440.4\n",
       "1993-...   \n",
       "3  Period\n",
       "1993-07    575.5\n",
       "1993-08    407.6\n",
       "1993-...   \n",
       "\n",
       "                                              y_pred  \n",
       "0  1992-01    266.0\n",
       "1992-02    145.9\n",
       "1992-03    1...  \n",
       "1  1992-07    270.953646\n",
       "1992-08    263.653646\n",
       "19...  \n",
       "2  1993-01    260.633333\n",
       "1993-02    215.833333\n",
       "19...  \n",
       "3  1993-07    378.659609\n",
       "1993-08    450.927848\n",
       "19...  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.forecasting.model_evaluation import evaluate\n",
    "\n",
    "y = load_shampoo_sales()\n",
    "results = evaluate(\n",
    "    forecaster=STLForecaster(sp=12),\n",
    "    y=y,\n",
    "    cv=ExpandingWindowSplitter(fh=[1, 2, 3, 4, 5, 6], initial_window=12, step_length=6),\n",
    "    scoring=MeanSquaredError(square_root=True),\n",
    "    return_data=True,\n",
    ")\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series(\n",
    "    y,\n",
    "    results[\"y_pred\"].iloc[0],\n",
    "    results[\"y_pred\"].iloc[1],\n",
    "    results[\"y_pred\"].iloc[2],\n",
    "    results[\"y_pred\"].iloc[3],\n",
    "    markers=[\"o\", \"\", \"\", \"\", \"\"],\n",
    "    labels=[\"y_true\"] + [\"y_pred (Backtest \" + str(x) + \")\" for x in range(4)],\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoML-like pipeline with exog data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datasets import load_macroeconomic\n",
    "\n",
    "data = load_macroeconomic()\n",
    "y = data[\"unemp\"]\n",
    "X = data.drop(columns=[\"unemp\"])\n",
    "\n",
    "y_train, y_test, X_train, X_test = temporal_train_test_split(y, X, test_size=40)\n",
    "\n",
    "fh = ForecastingHorizon(np.arange(1, 41), is_relative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = ExpandingWindowSplitter(fh=fh, initial_window=80, step_length=4)\n",
    "plot_windows(cv=cv, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datasets import load_macroeconomic\n",
    "from sktime.forecasting.statsforecast import StatsForecastAutoARIMA\n",
    "from sktime.split import temporal_train_test_split\n",
    "\n",
    "data = load_macroeconomic()\n",
    "y = data[\"unemp\"]\n",
    "X = data.drop(columns=[\"unemp\"])\n",
    "\n",
    "y_train, y_test, X_train, X_test = temporal_train_test_split(y, X, test_size=40)\n",
    "fh = ForecastingHorizon(np.arange(1, 41), is_relative=True)\n",
    "\n",
    "pipe_y = TransformedTargetForecaster(\n",
    "    steps=[\n",
    "        (\"detrender\", OptionalPassthrough(Detrender())),\n",
    "        (\"deseasonalizer\", OptionalPassthrough(Deseasonalizer())),\n",
    "        (\"scaler\", OptionalPassthrough(TabularToSeriesAdaptor(RobustScaler()))),\n",
    "        (\"forecaster\", StatsForecastAutoARIMA(sp=4)),\n",
    "    ]\n",
    ")\n",
    "permuted_y = Permute(pipe_y, permutation=None)\n",
    "\n",
    "pipe_X = TransformedTargetForecaster(\n",
    "    steps=[\n",
    "        (\"detrender\", OptionalPassthrough(Detrender())),\n",
    "        (\"deseasonalizer\", OptionalPassthrough(Deseasonalizer())),\n",
    "        (\"scaler\", OptionalPassthrough(TabularToSeriesAdaptor(RobustScaler()))),\n",
    "        (\"forecaster\", permuted_y),\n",
    "    ]\n",
    ")\n",
    "permuted_X = Permute(pipe_X, permutation=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ForecastingGridSearchCV(cv=ExpandingWindowSplitter(fh=ForecastingHorizon([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "            18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "            35, 36, 37, 38, 39, 40],\n",
       "           dtype=&#x27;int64&#x27;, is_relative=True),\n",
       "                                                   initial_window=80,\n",
       "                                                   step_length=4),\n",
       "                        error_score=&#x27;raise&#x27;,\n",
       "                        forecaster=Permute(estimator=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                                                         Op...\n",
       "                                    &#x27;estimator__forecaster__permutation&#x27;: [[&#x27;detrender&#x27;,\n",
       "                                                                            &#x27;deseasonalizer&#x27;,\n",
       "                                                                            &#x27;scaler&#x27;,\n",
       "                                                                            &#x27;forecaster&#x27;],\n",
       "                                                                           [&#x27;scaler&#x27;,\n",
       "                                                                            &#x27;detrender&#x27;,\n",
       "                                                                            &#x27;deseasonalizer&#x27;,\n",
       "                                                                            &#x27;forecaster&#x27;]],\n",
       "                                    &#x27;estimator__scaler__passthrough&#x27;: [True,\n",
       "                                                                       False],\n",
       "                                    &#x27;permutation&#x27;: [[&#x27;detrender&#x27;,\n",
       "                                                     &#x27;deseasonalizer&#x27;, &#x27;scaler&#x27;,\n",
       "                                                     &#x27;forecaster&#x27;],\n",
       "                                                    [&#x27;scaler&#x27;, &#x27;detrender&#x27;,\n",
       "                                                     &#x27;deseasonalizer&#x27;,\n",
       "                                                     &#x27;forecaster&#x27;]]},\n",
       "                        scoring=MeanSquaredError(square_root=True))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ForecastingGridSearchCV</label><div class=\"sk-toggleable__content\"><pre>ForecastingGridSearchCV(cv=ExpandingWindowSplitter(fh=ForecastingHorizon([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "            18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "            35, 36, 37, 38, 39, 40],\n",
       "           dtype=&#x27;int64&#x27;, is_relative=True),\n",
       "                                                   initial_window=80,\n",
       "                                                   step_length=4),\n",
       "                        error_score=&#x27;raise&#x27;,\n",
       "                        forecaster=Permute(estimator=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                                                         Op...\n",
       "                                    &#x27;estimator__forecaster__permutation&#x27;: [[&#x27;detrender&#x27;,\n",
       "                                                                            &#x27;deseasonalizer&#x27;,\n",
       "                                                                            &#x27;scaler&#x27;,\n",
       "                                                                            &#x27;forecaster&#x27;],\n",
       "                                                                           [&#x27;scaler&#x27;,\n",
       "                                                                            &#x27;detrender&#x27;,\n",
       "                                                                            &#x27;deseasonalizer&#x27;,\n",
       "                                                                            &#x27;forecaster&#x27;]],\n",
       "                                    &#x27;estimator__scaler__passthrough&#x27;: [True,\n",
       "                                                                       False],\n",
       "                                    &#x27;permutation&#x27;: [[&#x27;detrender&#x27;,\n",
       "                                                     &#x27;deseasonalizer&#x27;, &#x27;scaler&#x27;,\n",
       "                                                     &#x27;forecaster&#x27;],\n",
       "                                                    [&#x27;scaler&#x27;, &#x27;detrender&#x27;,\n",
       "                                                     &#x27;deseasonalizer&#x27;,\n",
       "                                                     &#x27;forecaster&#x27;]]},\n",
       "                        scoring=MeanSquaredError(square_root=True))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">forecaster: Permute</label><div class=\"sk-toggleable__content\"><pre>Permute(estimator=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                      OptionalPassthrough(transformer=Detrender())),\n",
       "                                                     (&#x27;deseasonalizer&#x27;,\n",
       "                                                      OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                                     (&#x27;scaler&#x27;,\n",
       "                                                      OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                                     (&#x27;forecaster&#x27;,\n",
       "                                                      Permute(estimator=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                                                                            OptionalPassthrough(transformer=Detrender())),\n",
       "                                                                                                           (&#x27;deseasonalizer&#x27;,\n",
       "                                                                                                            OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                                                                                           (&#x27;scaler&#x27;,\n",
       "                                                                                                            OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                                                                                           (&#x27;forecaster&#x27;,\n",
       "                                                                                                            StatsForecastAutoARIMA(sp=4))])))]))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Detrender())),\n",
       "                                   (&#x27;deseasonalizer&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                   (&#x27;scaler&#x27;,\n",
       "                                    OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                   (&#x27;forecaster&#x27;,\n",
       "                                    Permute(estimator=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                                                          OptionalPassthrough(transformer=Detrender())),\n",
       "                                                                                         (&#x27;deseasonalizer&#x27;,\n",
       "                                                                                          OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                                                                         (&#x27;scaler&#x27;,\n",
       "                                                                                          OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                                                                         (&#x27;forecaster&#x27;,\n",
       "                                                                                          StatsForecastAutoARIMA(sp=4))])))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TransformedTargetForecaster</label><div class=\"sk-toggleable__content\"><pre>TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Detrender())),\n",
       "                                   (&#x27;deseasonalizer&#x27;,\n",
       "                                    OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                   (&#x27;scaler&#x27;,\n",
       "                                    OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                   (&#x27;forecaster&#x27;,\n",
       "                                    Permute(estimator=TransformedTargetForecaster(steps=[(&#x27;detrender&#x27;,\n",
       "                                                                                          OptionalPassthrough(transformer=Detrender())),\n",
       "                                                                                         (&#x27;deseasonalizer&#x27;,\n",
       "                                                                                          OptionalPassthrough(transformer=Deseasonalizer())),\n",
       "                                                                                         (&#x27;scaler&#x27;,\n",
       "                                                                                          OptionalPassthrough(transformer=TabularToSeriesAdaptor(transformer=RobustScaler()))),\n",
       "                                                                                         (&#x27;forecaster&#x27;,\n",
       "                                                                                          StatsForecastAutoARIMA(sp=4))])))])</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ForecastingGridSearchCV(cv=ExpandingWindowSplitter(fh=ForecastingHorizon([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "            18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "            35, 36, 37, 38, 39, 40],\n",
       "           dtype='int64', is_relative=True),\n",
       "                                                   initial_window=80,\n",
       "                                                   step_length=4),\n",
       "                        error_score='raise',\n",
       "                        forecaster=Permute(estimator=TransformedTargetForecaster(steps=[('detrender',\n",
       "                                                                                         Op...\n",
       "                                    'estimator__forecaster__permutation': [['detrender',\n",
       "                                                                            'deseasonalizer',\n",
       "                                                                            'scaler',\n",
       "                                                                            'forecaster'],\n",
       "                                                                           ['scaler',\n",
       "                                                                            'detrender',\n",
       "                                                                            'deseasonalizer',\n",
       "                                                                            'forecaster']],\n",
       "                                    'estimator__scaler__passthrough': [True,\n",
       "                                                                       False],\n",
       "                                    'permutation': [['detrender',\n",
       "                                                     'deseasonalizer', 'scaler',\n",
       "                                                     'forecaster'],\n",
       "                                                    ['scaler', 'detrender',\n",
       "                                                     'deseasonalizer',\n",
       "                                                     'forecaster']]},\n",
       "                        scoring=MeanSquaredError(square_root=True))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv = ForecastingGridSearchCV(\n",
    "    forecaster=permuted_X,\n",
    "    param_grid={\n",
    "        \"estimator__forecaster__permutation\": [\n",
    "            [\"detrender\", \"deseasonalizer\", \"scaler\", \"forecaster\"],\n",
    "            [\"scaler\", \"detrender\", \"deseasonalizer\", \"forecaster\"],\n",
    "        ],\n",
    "        \"permutation\": [\n",
    "            [\"detrender\", \"deseasonalizer\", \"scaler\", \"forecaster\"],\n",
    "            [\"scaler\", \"detrender\", \"deseasonalizer\", \"forecaster\"],\n",
    "        ],\n",
    "        \"estimator__forecaster__estimator__scaler__passthrough\": [True, False],\n",
    "        \"estimator__scaler__passthrough\": [True, False],\n",
    "    },\n",
    "    cv=cv,\n",
    "    error_score=\"raise\",\n",
    "    scoring=MeanSquaredError(square_root=True),\n",
    ")\n",
    "\n",
    "gscv.fit(y=y_train, X=X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9856591203311482"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.cv_results_[\"mean_test_MeanSquaredError\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1207628182179863"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.cv_results_[\"mean_test_MeanSquaredError\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'estimator__forecaster__estimator__scaler__passthrough': True,\n",
       " 'estimator__forecaster__permutation': ['detrender',\n",
       "  'deseasonalizer',\n",
       "  'scaler',\n",
       "  'forecaster'],\n",
       " 'estimator__scaler__passthrough': False,\n",
       " 'permutation': ['detrender', 'deseasonalizer', 'scaler', 'forecaster']}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'estimator__forecaster__estimator__scaler__passthrough': True,\n",
       " 'estimator__forecaster__permutation': ['scaler',\n",
       "  'detrender',\n",
       "  'deseasonalizer',\n",
       "  'forecaster'],\n",
       " 'estimator__scaler__passthrough': True,\n",
       " 'permutation': ['scaler', 'detrender', 'deseasonalizer', 'forecaster']}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# worst params\n",
    "gscv.cv_results_.sort_values(by=\"mean_test_MeanSquaredError\", ascending=True).iloc[-1][\n",
    "    \"params\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Credits: notebook - pipelines\n",
    "\n",
    "notebook creation: aiwalter\n",
    "\n",
    "forecaster pipelines: fkiraly, aiwalter\\\n",
    "transformer pipelines & compositors: fkiraly, mloning, miraep8\\\n",
    "dunder interface: fkiraly, miraep8\\\n",
    "\n",
    "tuning, autoML: mloning, fkiraly, aiwalter\\\n",
    "CV and splitters: mloning, kkoralturk, khrapovs\\\n",
    "forecasting metrics: mloning, aiwalter, rnkuhns, fkiraly\\\n",
    "backtesting, evaluation: aiwalter, mloning, fkiraly, topher-lo"
   ]
  }
 ],
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